VAULT: vault accuracy using deep learning technology: new image-based artificial intelligence model for predicting implantable collamer lens postoperative vault.

PURPOSE: To develop an accurate deep learning model to predict postoperative vault of phakic implantable collamer lenses (ICLs).

SETTING

Parkhurst NuVision LASIK Eye Surgery, San Antonio, Texas.

DESIGN

Retrospective machine learning study.

METHODS

437 eyes of 221 consecutive patients who underwent ICL implantation were included. A neural network was trained on preoperative very high-frequency digital ultrasound images, patient demographics, and postoperative vault.

RESULTS

3059 images from 437 eyes of 221 patients were used to train the algorithm on individual ICL sizes. The 13.7 mm size was excluded because of insufficient data. A mean absolute error of 66.3 μm, 103 μm, and 91.8 μm were achieved with 100%, 99.0%, and 96.6% of predictions within 500 μm for the 12.1 mm, 12.6 mm, and 13.2 mm sizes, respectively.

CONCLUSIONS

This deep learning model achieved a high level of accuracy of predicting postoperative ICL vault with the overwhelming majority of predictions successfully within a clinically acceptable margin of vault.